{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generative Adversarial Network\n",
    "\n",
    "In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits!\n",
    "\n",
    "GANs were [first reported on](https://arxiv.org/abs/1406.2661) in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exploded in popularity. Here are a few examples to check out:\n",
    "\n",
    "* [Pix2Pix](https://affinelayer.com/pixsrv/) \n",
    "* [CycleGAN](https://github.com/junyanz/CycleGAN)\n",
    "* [A whole list](https://github.com/wiseodd/generative-models)\n",
    "\n",
    "The idea behind GANs is that you have two networks, a generator $G$ and a discriminator $D$, competing against each other. The generator makes fake data to pass to the discriminator. The discriminator also sees real data and predicts if the data it's received is real or fake. The generator is trained to fool the discriminator, it wants to output data that looks _as close as possible_ to real data. And the discriminator is trained to figure out which data is real and which is fake. What ends up happening is that the generator learns to make data that is indistiguishable from real data to the discriminator.\n",
    "\n",
    "![GAN diagram](assets/gan_diagram.png)\n",
    "\n",
    "The general structure of a GAN is shown in the diagram above, using MNIST images as data. The latent sample is a random vector the generator uses to contruct it's fake images. As the generator learns through training, it figures out how to map these random vectors to recognizable images that can foold the discriminator.\n",
    "\n",
    "The output of the discriminator is a sigmoid function, where 0 indicates a fake image and 1 indicates an real image. If you're interested only in generating new images, you can throw out the discriminator after training. Now, let's see how we build this thing in TensorFlow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pickle as pkl\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets('MNIST_data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Inputs\n",
    "\n",
    "First we need to create the inputs for our graph. We need two inputs, one for the discriminator and one for the generator. Here we'll call the discriminator input `inputs_real` and the generator input `inputs_z`. We'll assign them the appropriate sizes for each of the networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model_inputs(real_dim, z_dim):\n",
    "    inputs_real = tf.placeholder(tf.float32, (None, real_dim), name='input_real') \n",
    "    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')\n",
    "    \n",
    "    return inputs_real, inputs_z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generator network\n",
    "\n",
    "![GAN Network](assets/gan_network.png)\n",
    "\n",
    "Here we'll build the generator network. To make this network a universal function approximator, we'll need at least one hidden layer. We should use a leaky ReLU to allow gradients to flow backwards through the layer unimpeded. A leaky ReLU is like a normal ReLU, except that there is a small non-zero output for negative input values.\n",
    "\n",
    "#### Variable Scope\n",
    "Here we need to use `tf.variable_scope` for two reasons. Firstly, we're going to make sure all the variable names start with `generator`. Similarly, we'll prepend `discriminator` to the discriminator variables. This will help out later when we're training the separate networks.\n",
    "\n",
    "We could just use `tf.name_scope` to set the names, but we also want to reuse these networks with different inputs. For the generator, we're going to train it, but also _sample from it_ as we're training and after training. The discriminator will need to share variables between the fake and real input images. So, we can use the `reuse` keyword for `tf.variable_scope` to tell TensorFlow to reuse the variables instead of creating new ones if we build the graph again.\n",
    "\n",
    "To use `tf.variable_scope`, you use a `with` statement:\n",
    "```python\n",
    "with tf.variable_scope('scope_name', reuse=False):\n",
    "    # code here\n",
    "```\n",
    "\n",
    "Here's more from [the TensorFlow documentation](https://www.tensorflow.org/programmers_guide/variable_scope#the_problem) to get another look at using `tf.variable_scope`.\n",
    "\n",
    "#### Leaky ReLU\n",
    "TensorFlow doesn't provide an operation for leaky ReLUs, so we'll need to make one . For this you can use take the outputs from a linear fully connected layer and pass them to `tf.maximum`. Typically, a parameter `alpha` sets the magnitude of the output for negative values. So, the output for negative input (`x`) values is `alpha*x`, and the output for positive `x` is `x`:\n",
    "$$\n",
    "f(x) = max(\\alpha * x, x)\n",
    "$$\n",
    "\n",
    "#### Tanh Output\n",
    "The generator has been found to perform the best with $tanh$ for the generator output. This means that we'll have to rescale the MNIST images to be between -1 and 1, instead of 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generator(z, out_dim, n_units=128, reuse=False, alpha=0.01):\n",
    "    with tf.variable_scope('generator', reuse=reuse):\n",
    "        # Hidden layer\n",
    "        h1 = tf.layers.dense(z, n_units, activation=None)\n",
    "        # Leaky ReLU\n",
    "        h1 = tf.maximum(alpha * h1, h1)\n",
    "        \n",
    "        # Logits and tanh output\n",
    "        logits = tf.layers.dense(h1, out_dim, activation=None)\n",
    "        out = tf.tanh(logits)\n",
    "        \n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discriminator\n",
    "\n",
    "The discriminator network is almost exactly the same as the generator network, except that we're using a sigmoid output layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def discriminator(x, n_units=128, reuse=False, alpha=0.01):\n",
    "    with tf.variable_scope('discriminator', reuse=reuse):\n",
    "        # Hidden layer\n",
    "        h1 = tf.layers.dense(x, n_units, activation=None)\n",
    "        # Leaky ReLU\n",
    "        h1 = tf.maximum(alpha * h1, h1)\n",
    "        \n",
    "        logits = tf.layers.dense(h1, 1, activation=None)\n",
    "        out = tf.sigmoid(logits)\n",
    "        \n",
    "        return out, logits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Size of input image to discriminator\n",
    "input_size = 784\n",
    "# Size of latent vector to generator\n",
    "z_size = 100\n",
    "# Sizes of hidden layers in generator and discriminator\n",
    "g_hidden_size = 128\n",
    "d_hidden_size = 128\n",
    "# Leak factor for leaky ReLU\n",
    "alpha = 0.01\n",
    "# Smoothing \n",
    "smooth = 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build network\n",
    "\n",
    "Now we're building the network from the functions defined above.\n",
    "\n",
    "First is to get our inputs, `input_real, input_z` from `model_inputs` using the sizes of the input and z.\n",
    "\n",
    "Then, we'll create the generator, `generator(input_z, input_size)`. This builds the generator with the appropriate input and output sizes.\n",
    "\n",
    "Then the discriminators. We'll build two of them, one for real data and one for fake data. Since we want the weights to be the same for both real and fake data, we need to reuse the variables. For the fake data, we're getting it from the generator as `g_model`. So the real data discriminator is `discriminator(input_real)` while the fake discriminator is `discriminator(g_model, reuse=True)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "# Create our input placeholders\n",
    "input_real, input_z = model_inputs(input_size, z_size)\n",
    "\n",
    "# Build the model\n",
    "g_model = generator(input_z, input_size, n_units=g_hidden_size, alpha=alpha)\n",
    "# g_model is the generator output\n",
    "\n",
    "d_model_real, d_logits_real = discriminator(input_real, n_units=d_hidden_size, alpha=alpha)\n",
    "d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, n_units=d_hidden_size, alpha=alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discriminator and Generator Losses\n",
    "\n",
    "Now we need to calculate the losses, which is a little tricky. For the discriminator, the total loss is the sum of the losses for real and fake images, `d_loss = d_loss_real + d_loss_fake`. The losses will by sigmoid cross-entropys, which we can get with `tf.nn.sigmoid_cross_entropy_with_logits`. We'll also wrap that in `tf.reduce_mean` to get the mean for all the images in the batch. So the losses will look something like \n",
    "\n",
    "```python\n",
    "tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels))\n",
    "```\n",
    "\n",
    "For the real image logits, we'll use `d_logits_real` which we got from the discriminator in the cell above. For the labels, we want them to be all ones, since these are all real images. To help the discriminator generalize better, the labels are reduced a bit from 1.0 to 0.9, for example,  using the parameter `smooth`. This is known as label smoothing, typically used with classifiers to improve performance. In TensorFlow, it looks something like `labels = tf.ones_like(tensor) * (1 - smooth)`\n",
    "\n",
    "The discriminator loss for the fake data is similar. The logits are `d_logits_fake`, which we got from passing the generator output to the discriminator. These fake logits are used with labels of all zeros. Remember that we want the discriminator to output 1 for real images and 0 for fake images, so we need to set up the losses to reflect that.\n",
    "\n",
    "Finally, the generator losses are using `d_logits_fake`, the fake image logits. But, now the labels are all ones. The generator is trying to fool the discriminator, so it wants to discriminator to output ones for fake images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Calculate losses\n",
    "d_loss_real = tf.reduce_mean(\n",
    "                  tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, \n",
    "                                                          labels=tf.ones_like(d_logits_real) * (1 - smooth)))\n",
    "d_loss_fake = tf.reduce_mean(\n",
    "                  tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, \n",
    "                                                          labels=tf.zeros_like(d_logits_real)))\n",
    "d_loss = d_loss_real + d_loss_fake\n",
    "\n",
    "g_loss = tf.reduce_mean(\n",
    "             tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,\n",
    "                                                     labels=tf.ones_like(d_logits_fake)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizers\n",
    "\n",
    "We want to update the generator and discriminator variables separately. So we need to get the variables for each part build optimizers for the two parts. To get all the trainable variables, we use `tf.trainable_variables()`. This creates a list of all the variables we've defined in our graph.\n",
    "\n",
    "For the generator optimizer, we only want to generator variables. Our past selves were nice and used a variable scope to start all of our generator variable names with `generator`. So, we just need to iterate through the list from `tf.trainable_variables()` and keep variables to start with `generator`. Each variable object has an attribute `name` which holds the name of the variable as a string (`var.name == 'weights_0'` for instance). \n",
    "\n",
    "We can do something similar with the discriminator. All the variables in the discriminator start with `discriminator`.\n",
    "\n",
    "Then, in the optimizer we pass the variable lists to `var_list` in the `minimize` method. This tells the optimizer to only update the listed variables. Something like `tf.train.AdamOptimizer().minimize(loss, var_list=var_list)` will only train the variables in `var_list`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Optimizers\n",
    "learning_rate = 0.002\n",
    "\n",
    "# Get the trainable_variables, split into G and D parts\n",
    "t_vars = tf.trainable_variables()\n",
    "g_vars = [var for var in t_vars if var.name.startswith('generator')]\n",
    "d_vars = [var for var in t_vars if var.name.startswith('discriminator')]\n",
    "\n",
    "d_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(d_loss, var_list=d_vars)\n",
    "g_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(g_loss, var_list=g_vars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "batch_size = 100\n",
    "epochs = 100\n",
    "samples = []\n",
    "losses = []\n",
    "# Only save generator variables\n",
    "saver = tf.train.Saver(var_list=g_vars)\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    for e in range(epochs):\n",
    "        for ii in range(mnist.train.num_examples//batch_size):\n",
    "            batch = mnist.train.next_batch(batch_size)\n",
    "            \n",
    "            # Get images, reshape and rescale to pass to D\n",
    "            batch_images = batch[0].reshape((batch_size, 784))\n",
    "            batch_images = batch_images*2 - 1\n",
    "            \n",
    "            # Sample random noise for G\n",
    "            batch_z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
    "            \n",
    "            # Run optimizers\n",
    "            _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})\n",
    "            _ = sess.run(g_train_opt, feed_dict={input_z: batch_z})\n",
    "        \n",
    "        # At the end of each epoch, get the losses and print them out\n",
    "        train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_images})\n",
    "        train_loss_g = g_loss.eval({input_z: batch_z})\n",
    "            \n",
    "        print(\"Epoch {}/{}...\".format(e+1, epochs),\n",
    "              \"Discriminator Loss: {:.4f}...\".format(train_loss_d),\n",
    "              \"Generator Loss: {:.4f}\".format(train_loss_g))    \n",
    "        # Save losses to view after training\n",
    "        losses.append((train_loss_d, train_loss_g))\n",
    "        \n",
    "        # Sample from generator as we're training for viewing afterwards\n",
    "        sample_z = np.random.uniform(-1, 1, size=(16, z_size))\n",
    "        gen_samples = sess.run(\n",
    "                       generator(input_z, input_size, reuse=True),\n",
    "                       feed_dict={input_z: sample_z})\n",
    "        samples.append(gen_samples)\n",
    "        saver.save(sess, './checkpoints/generator.ckpt')\n",
    "\n",
    "# Save training generator samples\n",
    "with open('train_samples.pkl', 'wb') as f:\n",
    "    pkl.dump(samples, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training loss\n",
    "\n",
    "Here we'll check out the training losses for the generator and discriminator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f4e8947fbe0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PTB4BBt/J7vUFRmWRu22eeyHJPY1yl8kdINI0K/doigpVkQJ59Bm2efI6OCd1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\nP5nT2kOMMLngAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4e7fda4be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "losses = np.array(losses)\n",
    "plt.plot(losses.T[0], label='Discriminator')\n",
    "plt.plot(losses.T[1], label='Generator')\n",
    "plt.title(\"Training Losses\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generator samples from training\n",
    "\n",
    "Here we can view samples of images from the generator. First we'll look at images taken while training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def view_samples(epoch, samples):\n",
    "    fig, axes = plt.subplots(figsize=(7,7), nrows=4, ncols=4, sharey=True, sharex=True)\n",
    "    for ax, img in zip(axes.flatten(), samples[epoch]):\n",
    "        ax.xaxis.set_visible(False)\n",
    "        ax.yaxis.set_visible(False)\n",
    "        im = ax.imshow(img.reshape((28,28)), cmap='Greys_r')\n",
    "    \n",
    "    return fig, axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load samples from generator taken while training\n",
    "with open('train_samples.pkl', 'rb') as f:\n",
    "    samples = pkl.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are samples from the final training epoch. You can see the generator is able to reproduce numbers like 1, 7, 3, 2. Since this is just a sample, it isn't representative of the full range of images this generator can make."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4jl9++aXpWrVqmebYsYDR9/mqqo3G7CRmXvoYOXKk6cro5ZfWRuPzxTEt1LOT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\nCCGECML/A9aD2hgAgC7ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d1d1550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = view_samples(-1, samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below I'm showing the generated images as the network was training, every 10 epochs. With bonus optical illusion!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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33BBIdlGPi9wu5EK7OOyww2q4sH/D7nrP0/yKs7jkYp111uGPf/zjkHBRb420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maM1nK+rzkqIP3TKpwzzzwTSApFBWy2khXkdlvw/J933nkAzD777EDK7HrppZdGuP9V6p2WZ9yZ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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4e7c13e0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rows, cols = 10, 6\n",
    "fig, axes = plt.subplots(figsize=(7,12), nrows=rows, ncols=cols, sharex=True, sharey=True)\n",
    "\n",
    "for sample, ax_row in zip(samples[::int(len(samples)/rows)], axes):\n",
    "    for img, ax in zip(sample[::int(len(sample)/cols)], ax_row):\n",
    "        ax.imshow(img.reshape((28,28)), cmap='Greys_r')\n",
    "        ax.xaxis.set_visible(False)\n",
    "        ax.yaxis.set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It starts out as all noise. Then it learns to make only the center white and the rest black. You can start to see some number like structures appear out of the noise like 1s and 9s."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sampling from the generator\n",
    "\n",
    "We can also get completely new images from the generator by using the checkpoint we saved after training. We just need to pass in a new latent vector $z$ and we'll get new samples!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x6bj3EO8/fg5FqQkhhCgXaMIRQggRhLS1J0iFZJZx3IfWVaK6ZuPHjzcdtSxw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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4e80057c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "saver = tf.train.Saver(var_list=g_vars)\n",
    "with tf.Session() as sess:\n",
    "    saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\n",
    "    sample_z = np.random.uniform(-1, 1, size=(16, z_size))\n",
    "    gen_samples = sess.run(\n",
    "                   generator(input_z, input_size, reuse=True),\n",
    "                   feed_dict={input_z: sample_z})\n",
    "_ = view_samples(0, [gen_samples])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
